Report

Dataset statistics

Number of variables	14
Number of observations	1390
Missing cells	4429
Missing cells (%)	22.8%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	1.2 MiB
Average record size in memory	882.4 B

Variable types

Numeric	2
Text	12

Alerts

`department` has 151 (10.9%) missing values	Missing
`city` has 36 (2.6%) missing values	Missing
`state` has 972 (69.9%) missing values	Missing
`country` has 48 (3.5%) missing values	Missing
`zip code` has 918 (66.0%) missing values	Missing
`email` has 1221 (87.8%) missing values	Missing
`others` has 1072 (77.1%) missing values	Missing
`author_rank` has 190 (13.7%) zeros	Zeros

Reproduction

Analysis started	2024-06-01 19:39:02.519388
Analysis finished	2024-06-01 19:39:07.164801
Duration	4.65 seconds
Software version	ydata-profiling v4.8.3
Download configuration	config.json

pubmed_id
Real number (ℝ)

Distinct	148
Distinct (%)	10.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	37710110

Minimum	37596718
Maximum	37742171
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	11.0 KiB

Quantile statistics

Minimum	37596718
5-th percentile	37596786
Q1	37741761
median	37741914
Q3	37742025
95-th percentile	37742153
Maximum	37742171
Range	145453
Interquartile range (IQR)	264

Descriptive statistics

Standard deviation	60090.846
Coefficient of variation (CV)	0.0015934943
Kurtosis	-0.15778245
Mean	37710110
Median Absolute Deviation (MAD)	119
Skewness	-1.3573605
Sum	5.2417053 × 10¹⁰
Variance	3.6109097 × 10⁹
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
37596837	43	3.1%
37742025	33	2.4%
37741970	28	2.0%
37596786	27	1.9%
37742137	27	1.9%
37742093	26	1.9%
37596839	26	1.9%
37741822	24	1.7%
37741978	23	1.7%
37596793	22	1.6%
Other values (138)	1111	79.9%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
37596718	6	0.4%
37596774	16	1.2%
37596779	11	0.8%
37596780	4	0.3%
37596783	8	0.6%
37596784	2	0.1%
37596785	22	1.6%
37596786	27	1.9%
37596787	17	1.2%
37596790	6	0.4%

Value	Count	Frequency (%)
37742171	8	0.6%
37742170	3	0.2%
37742169	2	0.1%
37742168	7	0.5%
37742167	4	0.3%
37742166	7	0.5%
37742165	7	0.5%
37742163	13	0.9%
37742161	5	0.4%
37742160	4	0.3%

author_rank
Real number (ℝ)

ZEROS

Distinct	33
Distinct (%)	2.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.5928058

Minimum	0
Maximum	35
Zeros	190
Zeros (%)	13.7%
Negative	0
Negative (%)	0.0%
Memory size	11.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	3.5
Q3	7
95-th percentile	12
Maximum	35
Range	35
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	4.4902525
Coefficient of variation (CV)	0.97767088
Kurtosis	7.931301
Mean	4.5928058
Median Absolute Deviation (MAD)	2.5
Skewness	2.1217082
Sum	6384
Variance	20.162367
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=33)

Value	Count	Frequency (%)
0	190	13.7%
1	181	13.0%
2	168	12.1%
3	156	11.2%
4	131	9.4%
5	115	8.3%
6	99	7.1%
7	71	5.1%
8	68	4.9%
9	51	3.7%
Other values (23)	160	11.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	190	13.7%
1	181	13.0%
2	168	12.1%
3	156	11.2%
4	131	9.4%
5	115	8.3%
6	99	7.1%
7	71	5.1%
8	68	4.9%
9	51	3.7%

Value	Count	Frequency (%)
35	1	0.1%
34	1	0.1%
33	1	0.1%
32	1	0.1%
31	1	0.1%
30	1	0.1%
29	1	0.1%
28	1	0.1%
24	2	0.1%
23	2	0.1%

last_name
Text

Distinct	776
Distinct (%)	55.8%
Missing	0
Missing (%)	0.0%
Memory size	87.2 KiB

Length

Max length	25
Median length	18
Mean length	5.9820144
Min length	2

Characters and Unicode

Total characters	8315
Distinct characters	70
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	503 ?
Unique (%)	36.2%

Sample

1st row	Talbot
2nd row	Talbot
3rd row	Perkins
4th row	Tallon
5th row	Dawkins

Value	Count	Frequency (%)
wang	31	2.2%
chen	25	1.7%
zhang	23	1.6%
liu	20	1.4%
li	18	1.3%
xu	15	1.0%
huang	12	0.8%
de	12	0.8%
yang	11	0.8%
tang	11	0.8%
Other values (786)	1257	87.6%

Most occurring characters

Value	Count	Frequency (%)
a	1078	13.0%
n	636	7.6%
e	630	7.6%
i	609	7.3%
r	460	5.5%
o	446	5.4%
u	384	4.6%
h	343	4.1%
l	287	3.5%
s	260	3.1%
Other values (60)	3182	38.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	8315	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	1078	13.0%
n	636	7.6%
e	630	7.6%
i	609	7.3%
r	460	5.5%
o	446	5.4%
u	384	4.6%
h	343	4.1%
l	287	3.5%
s	260	3.1%
Other values (60)	3182	38.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	8315	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	1078	13.0%
n	636	7.6%
e	630	7.6%
i	609	7.3%
r	460	5.5%
o	446	5.4%
u	384	4.6%
h	343	4.1%
l	287	3.5%
s	260	3.1%
Other values (60)	3182	38.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	8315	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	1078	13.0%
n	636	7.6%
e	630	7.6%
i	609	7.3%
r	460	5.5%
o	446	5.4%
u	384	4.6%
h	343	4.1%
l	287	3.5%
s	260	3.1%
Other values (60)	3182	38.3%

fore_name
Text

Distinct	913
Distinct (%)	65.7%
Missing	1
Missing (%)	0.1%
Memory size	87.1 KiB

Length

Max length	20
Median length	16
Mean length	6.5205184
Min length	1

Characters and Unicode

Total characters	9057
Distinct characters	63
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	609 ?
Unique (%)	43.8%

Sample

1st row	Jack S
2nd row	Jack S
3rd row	Dean R
4th row	Christine M
5th row	Tony G

Value	Count	Frequency (%)
m	56	3.3%
a	26	1.5%
j	18	1.1%
s	16	1.0%
c	15	0.9%
r	12	0.7%
d	12	0.7%
maría	11	0.7%
yu	10	0.6%
n	9	0.5%
Other values (919)	1493	89.0%

Most occurring characters

Value	Count	Frequency (%)
a	1105	12.2%
i	874	9.6%
n	820	9.1%
e	609	6.7%
o	468	5.2%
h	383	4.2%
r	367	4.1%
u	339	3.7%
	289	3.2%
g	281	3.1%
Other values (53)	3522	38.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	9057	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	1105	12.2%
i	874	9.6%
n	820	9.1%
e	609	6.7%
o	468	5.2%
h	383	4.2%
r	367	4.1%
u	339	3.7%
	289	3.2%
g	281	3.1%
Other values (53)	3522	38.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	9057	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	1105	12.2%
i	874	9.6%
n	820	9.1%
e	609	6.7%
o	468	5.2%
h	383	4.2%
r	367	4.1%
u	339	3.7%
	289	3.2%
g	281	3.1%
Other values (53)	3522	38.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	9057	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	1105	12.2%
i	874	9.6%
n	820	9.1%
e	609	6.7%
o	468	5.2%
h	383	4.2%
r	367	4.1%
u	339	3.7%
	289	3.2%
g	281	3.1%
Other values (53)	3522	38.9%

initials
Text

Distinct	197
Distinct (%)	14.2%
Missing	1
Missing (%)	0.1%
Memory size	79.3 KiB

Length

Max length	3
Median length	1
Mean length	1.2455004
Min length	1

Characters and Unicode

Total characters	1730
Distinct characters	29
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	94 ?
Unique (%)	6.8%

Sample

1st row	JS
2nd row	JS
3rd row	DR
4th row	CM
5th row	TG

Value	Count	Frequency (%)
s	111	8.0%
a	86	6.2%
y	86	6.2%
j	84	6.0%
m	83	6.0%
l	57	4.1%
h	54	3.9%
d	48	3.5%
k	47	3.4%
r	43	3.1%
Other values (187)	690	49.7%

Most occurring characters

Value	Count	Frequency (%)
M	188	10.9%
J	155	9.0%
S	149	8.6%
A	144	8.3%
Y	110	6.4%
R	81	4.7%
L	80	4.6%
H	77	4.5%
K	77	4.5%
D	76	4.4%
Other values (19)	593	34.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1730	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
M	188	10.9%
J	155	9.0%
S	149	8.6%
A	144	8.3%
Y	110	6.4%
R	81	4.7%
L	80	4.6%
H	77	4.5%
K	77	4.5%
D	76	4.4%
Other values (19)	593	34.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1730	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
M	188	10.9%
J	155	9.0%
S	149	8.6%
A	144	8.3%
Y	110	6.4%
R	81	4.7%
L	80	4.6%
H	77	4.5%
K	77	4.5%
D	76	4.4%
Other values (19)	593	34.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1730	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
M	188	10.9%
J	155	9.0%
S	149	8.6%
A	144	8.3%
Y	110	6.4%
R	81	4.7%
L	80	4.6%
H	77	4.5%
K	77	4.5%
D	76	4.4%
Other values (19)	593	34.3%

affiliation
Text

Distinct	746
Distinct (%)	53.7%
Missing	1
Missing (%)	0.1%
Memory size	259.4 KiB

Length

Max length	465
Median length	195
Mean length	120.14399
Min length	21

Characters and Unicode

Total characters	166880
Distinct characters	100
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	479 ?
Unique (%)	34.5%

Sample

1st row	Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK.
2nd row	Centre for Health, Activity and Wellbeing Research, Cardiff Metropolitan University, Cardiff, UK.
3rd row	Department of Sport Science, University of Innsbruck, Innsbruck, Austria.
4th row	Centre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, Canada.
5th row	Centre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, Canada.

Value	Count	Frequency (%)
of	1983	9.5%
university	898	4.3%
and	738	3.5%
department	509	2.4%
china	415	2.0%
medicine	332	1.6%
medical	313	1.5%
hospital	292	1.4%
laboratory	271	1.3%
research	269	1.3%
Other values (1852)	14905	71.2%

Most occurring characters

Value	Count	Frequency (%)
	19525	11.7%
e	12940	7.8%
a	12185	7.3%
i	11431	6.8%
n	10945	6.6%
o	9367	5.6%
t	8453	5.1%
r	7918	4.7%
,	5877	3.5%
l	5649	3.4%
Other values (90)	62590	37.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	166880	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	19525	11.7%
e	12940	7.8%
a	12185	7.3%
i	11431	6.8%
n	10945	6.6%
o	9367	5.6%
t	8453	5.1%
r	7918	4.7%
,	5877	3.5%
l	5649	3.4%
Other values (90)	62590	37.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	166880	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	19525	11.7%
e	12940	7.8%
a	12185	7.3%
i	11431	6.8%
n	10945	6.6%
o	9367	5.6%
t	8453	5.1%
r	7918	4.7%
,	5877	3.5%
l	5649	3.4%
Other values (90)	62590	37.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	166880	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	19525	11.7%
e	12940	7.8%
a	12185	7.3%
i	11431	6.8%
n	10945	6.6%
o	9367	5.6%
t	8453	5.1%
r	7918	4.7%
,	5877	3.5%
l	5649	3.4%
Other values (90)	62590	37.5%

department
Text

MISSING

Distinct	426
Distinct (%)	34.4%
Missing	151
Missing (%)	10.9%
Memory size	131.4 KiB

Length

Max length	416
Median length	83
Mean length	45.110573
Min length	4

Characters and Unicode

Total characters	55892
Distinct characters	75
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	171 ?
Unique (%)	13.8%

Sample

1st row	School of Sport and Health Sciences
2nd row	Centre for Health, Activity and Wellbeing Research
3rd row	Department of Sport Science
4th row	Centre for Heart, Lung and Vascular Health
5th row	Centre for Heart, Lung and Vascular Health

Value	Count	Frequency (%)
of	986	13.7%
department	477	6.6%
and	458	6.4%
laboratory	223	3.1%
key	178	2.5%
medicine	174	2.4%
for	138	1.9%
center	134	1.9%
research	113	1.6%
engineering	100	1.4%
Other values (591)	4212	58.6%

Most occurring characters

Value	Count	Frequency (%)
	5954	10.7%
e	5499	9.8%
o	4211	7.5%
a	4202	7.5%
n	3789	6.8%
t	3738	6.7%
i	3423	6.1%
r	3394	6.1%
l	2093	3.7%
c	1917	3.4%
Other values (65)	17672	31.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	55892	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	5954	10.7%
e	5499	9.8%
o	4211	7.5%
a	4202	7.5%
n	3789	6.8%
t	3738	6.7%
i	3423	6.1%
r	3394	6.1%
l	2093	3.7%
c	1917	3.4%
Other values (65)	17672	31.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	55892	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	5954	10.7%
e	5499	9.8%
o	4211	7.5%
a	4202	7.5%
n	3789	6.8%
t	3738	6.7%
i	3423	6.1%
r	3394	6.1%
l	2093	3.7%
c	1917	3.4%
Other values (65)	17672	31.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	55892	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	5954	10.7%
e	5499	9.8%
o	4211	7.5%
a	4202	7.5%
n	3789	6.8%
t	3738	6.7%
i	3423	6.1%
r	3394	6.1%
l	2093	3.7%
c	1917	3.4%
Other values (65)	17672	31.6%

name_of_university
Text

Distinct	418
Distinct (%)	30.2%
Missing	8
Missing (%)	0.6%
Memory size	124.6 KiB

Length

Max length	99
Median length	64
Mean length	31.719247
Min length	3

Characters and Unicode

Total characters	43836
Distinct characters	80
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	177 ?
Unique (%)	12.8%

Sample

1st row	Cardiff Metropolitan University
2nd row	Cardiff Metropolitan University
3rd row	University of Innsbruck
4th row	University of British Columbia Okanagan
5th row	University of British Columbia Okanagan

Value	Count	Frequency (%)
university	792	14.0%
of	484	8.5%
medical	215	3.8%
hospital	184	3.2%
and	163	2.9%
institute	120	2.1%
national	111	2.0%
research	99	1.7%
medicine	84	1.5%
center	81	1.4%
Other values (623)	3342	58.9%

Most occurring characters

Value	Count	Frequency (%)
	4293	9.8%
i	4193	9.6%
e	3652	8.3%
n	3185	7.3%
a	2976	6.8%
t	2669	6.1%
o	2392	5.5%
r	2321	5.3%
s	2006	4.6%
l	1635	3.7%
Other values (70)	14514	33.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	43836	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	4293	9.8%
i	4193	9.6%
e	3652	8.3%
n	3185	7.3%
a	2976	6.8%
t	2669	6.1%
o	2392	5.5%
r	2321	5.3%
s	2006	4.6%
l	1635	3.7%
Other values (70)	14514	33.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	43836	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	4293	9.8%
i	4193	9.6%
e	3652	8.3%
n	3185	7.3%
a	2976	6.8%
t	2669	6.1%
o	2392	5.5%
r	2321	5.3%
s	2006	4.6%
l	1635	3.7%
Other values (70)	14514	33.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	43836	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	4293	9.8%
i	4193	9.6%
e	3652	8.3%
n	3185	7.3%
a	2976	6.8%
t	2669	6.1%
o	2392	5.5%
r	2321	5.3%
s	2006	4.6%
l	1635	3.7%
Other values (70)	14514	33.1%

city
Text

MISSING

Distinct	266
Distinct (%)	19.6%
Missing	36
Missing (%)	2.6%
Memory size	86.9 KiB

Length

Max length	21
Median length	19
Mean length	7.2776957
Min length	4

Characters and Unicode

Total characters	9854
Distinct characters	63
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	87 ?
Unique (%)	6.4%

Sample

1st row	Cardiff
2nd row	Cardiff
3rd row	Innsbruck
4th row	Kelowna
5th row	Kelowna

Value	Count	Frequency (%)
wuhan	42	2.9%
hangzhou	39	2.7%
madrid	34	2.4%
nanning	32	2.2%
shanghai	27	1.9%
oxford	26	1.8%
kathmandu	24	1.7%
kunming	22	1.5%
rotterdam	22	1.5%
hohhot	21	1.5%
Other values (282)	1157	80.0%

Most occurring characters

Value	Count	Frequency (%)
a	1295	13.1%
n	1023	10.4%
i	665	6.7%
e	623	6.3%
o	593	6.0%
h	510	5.2%
r	490	5.0%
u	373	3.8%
g	368	3.7%
d	350	3.6%
Other values (53)	3564	36.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	9854	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	1295	13.1%
n	1023	10.4%
i	665	6.7%
e	623	6.3%
o	593	6.0%
h	510	5.2%
r	490	5.0%
u	373	3.8%
g	368	3.7%
d	350	3.6%
Other values (53)	3564	36.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	9854	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	1295	13.1%
n	1023	10.4%
i	665	6.7%
e	623	6.3%
o	593	6.0%
h	510	5.2%
r	490	5.0%
u	373	3.8%
g	368	3.7%
d	350	3.6%
Other values (53)	3564	36.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	9854	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	1295	13.1%
n	1023	10.4%
i	665	6.7%
e	623	6.3%
o	593	6.0%
h	510	5.2%
r	490	5.0%
u	373	3.8%
g	368	3.7%
d	350	3.6%
Other values (53)	3564	36.2%

state
Text

MISSING

Distinct	75
Distinct (%)	17.9%
Missing	972
Missing (%)	69.9%
Memory size	56.2 KiB

Length

Max length	20
Median length	17
Mean length	5.8971292
Min length	1

Characters and Unicode

Total characters	2465
Distinct characters	51
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	15 ?
Unique (%)	3.6%

Sample

1st row	Ohio
2nd row	Dolnoslaskie
3rd row	Wielkopolskie
4th row	Lubuskie
5th row	Wielkopolskie

Value	Count	Frequency (%)
ca	45	9.0%
province	26	5.2%
jiangsu	23	4.6%
new	19	3.8%
western	17	3.4%
md	16	3.2%
hubei	16	3.2%
mongolia	15	3.0%
ny	15	3.0%
inner	15	3.0%
Other values (70)	292	58.5%

Most occurring characters

Value	Count	Frequency (%)
n	225	9.1%
i	209	8.5%
a	204	8.3%
e	178	7.2%
o	129	5.2%
r	119	4.8%
A	100	4.1%
g	92	3.7%
s	83	3.4%
	81	3.3%
Other values (41)	1045	42.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2465	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
n	225	9.1%
i	209	8.5%
a	204	8.3%
e	178	7.2%
o	129	5.2%
r	119	4.8%
A	100	4.1%
g	92	3.7%
s	83	3.4%
	81	3.3%
Other values (41)	1045	42.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2465	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
n	225	9.1%
i	209	8.5%
a	204	8.3%
e	178	7.2%
o	129	5.2%
r	119	4.8%
A	100	4.1%
g	92	3.7%
s	83	3.4%
	81	3.3%
Other values (41)	1045	42.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2465	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
n	225	9.1%
i	209	8.5%
a	204	8.3%
e	178	7.2%
o	129	5.2%
r	119	4.8%
A	100	4.1%
g	92	3.7%
s	83	3.4%
	81	3.3%
Other values (41)	1045	42.4%

country
Text

MISSING

Distinct	60
Distinct (%)	4.5%
Missing	48
Missing (%)	3.5%
Memory size	84.7 KiB

Length

Max length	32
Median length	26
Mean length	6.3703428
Min length	2

Characters and Unicode

Total characters	8549
Distinct characters	48
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	10 ?
Unique (%)	0.7%

Sample

1st row	UK
2nd row	UK
3rd row	Austria
4th row	Canada
5th row	Canada

Value	Count	Frequency (%)
china	365	22.3%
usa	190	11.6%
japan	89	5.4%
spain	57	3.5%
nepal	57	3.5%
germany	53	3.2%
of	51	3.1%
republic	50	3.1%
uk	48	2.9%
canada	47	2.9%
Other values (54)	629	38.4%

Most occurring characters

Value	Count	Frequency (%)
a	1305	15.3%
n	879	10.3%
i	680	8.0%
e	503	5.9%
h	436	5.1%
C	425	5.0%
l	323	3.8%
p	307	3.6%
	294	3.4%
S	291	3.4%
Other values (38)	3106	36.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	8549	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	1305	15.3%
n	879	10.3%
i	680	8.0%
e	503	5.9%
h	436	5.1%
C	425	5.0%
l	323	3.8%
p	307	3.6%
	294	3.4%
S	291	3.4%
Other values (38)	3106	36.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	8549	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	1305	15.3%
n	879	10.3%
i	680	8.0%
e	503	5.9%
h	436	5.1%
C	425	5.0%
l	323	3.8%
p	307	3.6%
	294	3.4%
S	291	3.4%
Other values (38)	3106	36.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	8549	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	1305	15.3%
n	879	10.3%
i	680	8.0%
e	503	5.9%
h	436	5.1%
C	425	5.0%
l	323	3.8%
p	307	3.6%
	294	3.4%
S	291	3.4%
Other values (38)	3106	36.3%

zip code
Text

MISSING

Distinct	105
Distinct (%)	22.2%
Missing	918
Missing (%)	66.0%
Memory size	57.7 KiB

Length

Max length	12
Median length	6
Mean length	5.7478814
Min length	2

Characters and Unicode

Total characters	2713
Distinct characters	35
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	36 ?
Unique (%)	7.6%

Sample

1st row	10
2nd row	10
3rd row	10
4th row	10
5th row	10

Value	Count	Frequency (%)
430070	42	8.5%
310006	25	5.0%
200011	23	4.6%
650093	22	4.4%
ox3	18	3.6%
90033	18	3.6%
550001	17	3.4%
310016	13	2.6%
28034	13	2.6%
28029	12	2.4%
Other values (99)	293	59.1%

Most occurring characters

Value	Count	Frequency (%)
0	861	31.7%
1	375	13.8%
3	323	11.9%
2	185	6.8%
5	165	6.1%
4	138	5.1%
6	130	4.8%
7	125	4.6%
8	119	4.4%
9	110	4.1%
Other values (25)	182	6.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2713	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	861	31.7%
1	375	13.8%
3	323	11.9%
2	185	6.8%
5	165	6.1%
4	138	5.1%
6	130	4.8%
7	125	4.6%
8	119	4.4%
9	110	4.1%
Other values (25)	182	6.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2713	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	861	31.7%
1	375	13.8%
3	323	11.9%
2	185	6.8%
5	165	6.1%
4	138	5.1%
6	130	4.8%
7	125	4.6%
8	119	4.4%
9	110	4.1%
Other values (25)	182	6.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2713	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	861	31.7%
1	375	13.8%
3	323	11.9%
2	185	6.8%
5	165	6.1%
4	138	5.1%
6	130	4.8%
7	125	4.6%
8	119	4.4%
9	110	4.1%
Other values (25)	182	6.7%

email
Text

MISSING

Distinct	117
Distinct (%)	69.2%
Missing	1221
Missing (%)	87.8%
Memory size	51.1 KiB

Length

Max length	36
Median length	30
Mean length	20.852071
Min length	12

Characters and Unicode

Total characters	3524
Distinct characters	48
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	84 ?
Unique (%)	49.7%

Sample

1st row	marcin.zawadzki@umed.wroc.pl
2nd row	skropins@ump.edu.pl
3rd row	wojciechleppert@wp.pl
4th row	kwt@tobis.pl
5th row	njmulss@njmu.edu.cn

Value	Count	Frequency (%)
xinquanjiang2011@126.com	4	2.4%
natalie.claunch@usda.gov	4	2.4%
xu_lei@kust.edu.cn	4	2.4%
haoew@gxtcmu.edu.cn	3	1.8%
gsxiong@njau.edu.cn	3	1.8%
afelipe@ub.edu	3	1.8%
bryansolitude@gmail.com	3	1.8%
huangxiaolong@gznu.edu.cn	3	1.8%
hychen_1201@163.com	3	1.8%
paul.habert@ap-hm.fr	3	1.8%
Other values (107)	136	80.5%

Most occurring characters

Value	Count	Frequency (%)
.	285	8.1%
a	269	7.6%
e	218	6.2%
n	214	6.1%
i	212	6.0%
o	200	5.7%
u	199	5.6%
m	183	5.2%
c	180	5.1%
@	169	4.8%
Other values (38)	1395	39.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3524	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	285	8.1%
a	269	7.6%
e	218	6.2%
n	214	6.1%
i	212	6.0%
o	200	5.7%
u	199	5.6%
m	183	5.2%
c	180	5.1%
@	169	4.8%
Other values (38)	1395	39.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3524	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	285	8.1%
a	269	7.6%
e	218	6.2%
n	214	6.1%
i	212	6.0%
o	200	5.7%
u	199	5.6%
m	183	5.2%
c	180	5.1%
@	169	4.8%
Other values (38)	1395	39.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3524	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	285	8.1%
a	269	7.6%
e	218	6.2%
n	214	6.1%
i	212	6.0%
o	200	5.7%
u	199	5.6%
m	183	5.2%
c	180	5.1%
@	169	4.8%
Other values (38)	1395	39.6%

others
Text

MISSING

Distinct	143
Distinct (%)	45.0%
Missing	1072
Missing (%)	77.1%
Memory size	71.7 KiB

Length

Max length	271
Median length	86
Mean length	56.5
Min length	4

Characters and Unicode

Total characters	17967
Distinct characters	87
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	80 ?
Unique (%)	25.2%

Sample

1st row	School of Physics and Astronomy
2nd row	School of Physics and Astronomy
3rd row	School of Physics and Astronomy
4th row	School of Physics and Astronomy
5th row	School of Physics and Astronomy

Value	Count	Frequency (%)
of	247	10.3%
and	90	3.7%
research	75	3.1%
center	57	2.4%
university	54	2.2%
zhejiang	52	2.2%
hospital	51	2.1%
school	42	1.7%
oral	39	1.6%
for	37	1.5%
Other values (409)	1660	69.1%

Most occurring characters

Value	Count	Frequency (%)
	2110	11.7%
e	1468	8.2%
a	1307	7.3%
i	1300	7.2%
n	1159	6.5%
o	1094	6.1%
t	945	5.3%
r	890	5.0%
s	681	3.8%
l	659	3.7%
Other values (77)	6354	35.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	17967	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	2110	11.7%
e	1468	8.2%
a	1307	7.3%
i	1300	7.2%
n	1159	6.5%
o	1094	6.1%
t	945	5.3%
r	890	5.0%
s	681	3.8%
l	659	3.7%
Other values (77)	6354	35.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	17967	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	2110	11.7%
e	1468	8.2%
a	1307	7.3%
i	1300	7.2%
n	1159	6.5%
o	1094	6.1%
t	945	5.3%
r	890	5.0%
s	681	3.8%
l	659	3.7%
Other values (77)	6354	35.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	17967	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	2110	11.7%
e	1468	8.2%
a	1307	7.3%
i	1300	7.2%
n	1159	6.5%
o	1094	6.1%
t	945	5.3%
r	890	5.0%
s	681	3.8%
l	659	3.7%
Other values (77)	6354	35.4%

pubmed_id
author_rank

author_rank
pubmed_id

author_rank
pubmed_id

Auto

Heatmap
Table

	author_rank	pubmed_id
author_rank	1.000	-0.104
pubmed_id	-0.104	1.000

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

First rows
Last rows

	pubmed_id	author_rank	last_name	fore_name	initials	affiliation	department	name_of_university	city	state	country	zip code	email	others
0	37742137	0	Talbot	Jack S	JS	Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK.	School of Sport and Health Sciences	Cardiff Metropolitan University	Cardiff	NaN	UK	NaN	NaN	NaN
1	37742137	0	Talbot	Jack S	JS	Centre for Health, Activity and Wellbeing Research, Cardiff Metropolitan University, Cardiff, UK.	Centre for Health, Activity and Wellbeing Research	Cardiff Metropolitan University	Cardiff	NaN	UK	NaN	NaN	NaN
2	37742137	1	Perkins	Dean R	DR	Department of Sport Science, University of Innsbruck, Innsbruck, Austria.	Department of Sport Science	University of Innsbruck	Innsbruck	NaN	Austria	NaN	NaN	NaN
3	37742137	2	Tallon	Christine M	CM	Centre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, Canada.	Centre for Heart, Lung and Vascular Health	University of British Columbia Okanagan	Kelowna	NaN	Canada	NaN	NaN	NaN
4	37742137	3	Dawkins	Tony G	TG	Centre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, Canada.	Centre for Heart, Lung and Vascular Health	University of British Columbia Okanagan	Kelowna	NaN	Canada	NaN	NaN	NaN
5	37742137	4	Douglas	Andrew J M	AJM	Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK.	School of Sport and Health Sciences	Cardiff Metropolitan University	Cardiff	NaN	UK	NaN	NaN	NaN
6	37742137	4	Douglas	Andrew J M	AJM	Centre for Health, Activity and Wellbeing Research, Cardiff Metropolitan University, Cardiff, UK.	Centre for Health, Activity and Wellbeing Research	Cardiff Metropolitan University	Cardiff	NaN	UK	NaN	NaN	NaN
7	37742137	5	Beckerleg	Ryan	R	Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, UK.	Brain Research Imaging Centre (CUBRIC)	Cardiff University	Cardiff	NaN	UK	NaN	NaN	School of Physics and Astronomy
8	37742137	6	Crofts	Andrew	A	Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, UK.	Brain Research Imaging Centre (CUBRIC)	Cardiff University	Cardiff	NaN	UK	NaN	NaN	School of Physics and Astronomy
9	37742137	7	Wright	Melissa E	ME	Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, UK.	Brain Research Imaging Centre (CUBRIC)	Cardiff University	Cardiff	NaN	UK	NaN	NaN	School of Physics and Astronomy

	pubmed_id	author_rank	last_name	fore_name	initials	affiliation	department	name_of_university	city	state	country	zip code	email	others
1380	37596774	6	Sinclair	Rodney D	RD	Sinclair Dermatology, Melbourne, Australia.	Dermatology	Dermatology	Melbourne	NaN	Australia	NaN	NaN	NaN
1381	37596774	6	Sinclair	Rodney D	RD	University of Melbourne, Melbourne, Australia.	NaN	University of Melbourne	Melbourne	NaN	Australia	NaN	NaN	NaN
1382	37596774	6	Sinclair	Rodney D	RD	Sinclair Dermatology.	NaN	Sinclair Dermatology	NaN	NaN	NaN	NaN	NaN	NaN
1383	37596774	6	Sinclair	Rodney D	RD	University of Melbourne, Melbourne,Australia.	NaN	University of Melbourne	Melbourne	NaN	Australia	NaN	NaN	NaN
1384	37596718	6	Xi	Shuiqing	S	Department of Cardiology, The Fourth Hospital of Harbin Medical University, 150000, Harbin, P. R. China.	Department of Cardiology	Harbin Medical University	Harbin	NaN	P. R. China	150000	NaN	The Fourth Hospital
1385	37596718	7	Hong	Xiaojian	X	Department of Cardiology, The Fourth Hospital of Harbin Medical University, 150000, Harbin, P. R. China.	Department of Cardiology	Harbin Medical University	Harbin	NaN	P. R. China	150000	NaN	The Fourth Hospital
1386	37596718	8	Zhou	Meifang	M	Department of Nuclear Medicine, The Fourth Hospital of Harbin Medical University, 150000, Harbin, P. R. China.	Department of Nuclear Medicine	Harbin Medical University	Harbin	NaN	P. R. China	150000	NaN	NaN
1387	37596718	8	Zhou	Meifang	M	NHC Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC) of Harbin Medical University, 150000, Harbin, P. R. China.	NHC Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC)	Harbin Medical University	Harbin	NaN	P. R. China	150000	NaN	NaN
1388	37596718	9	Wang	Haoyu	H	Department of Nuclear Medicine, The Fourth Hospital of Harbin Medical University, 150000, Harbin, P. R. China.	Department of Nuclear Medicine	Harbin Medical University	Harbin	NaN	P. R. China	150000	NaN	NaN
1389	37596718	9	Wang	Haoyu	H	NHC Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC) of Harbin Medical University, 150000, Harbin, P. R. China.	NHC Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC)	Harbin Medical University	Harbin	NaN	P. R. China	150000	NaN	NaN

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks